chest_lm

<code>'chest_lm'</code> is used to assess confounding effects using Linear Regression Models.
It presents linear regression coefficients as effect estimates and
changes when other variables are added sequentially to the model.

Applies the change-in-effect estimate method to assess confounding
effects in medical and epidemiological research (Greenland & Pearce (2016)
<doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model
including only the outcome and exposure variables. At each of the subsequent
steps, one variable which creates the largest change among the remaining variables
is selected. This process is repeated until all variables have been entered into
the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest'
package has functions for linear regression, logistic regression, negative
binomial regression, Cox proportional hazards model and conditional logistic
regression.

Zhiqiang Wang

chest

Change-in-Estimate Approach to Assess Confounding Effects

chest_lm function

<dl><dt>crude</dt>
<dd>An object of formula for initial model, generally crude model.
However, any other variables can also be included here as the initial model.</dd>
<dt>xlist</dt>
<dd>A vector of characters with all variable names of potential confounders.</dd>
<dt>data</dt>
<dd>Data frame.</dd>
<dt>method</dt>
<dd>The method to be used; see 'lm'.</dd>
<dt>na_omit</dt>
<dd>Remove all missing values.</dd>
<dt>indicate</dt>
<dd>indicate progress</dd>
<dt>plus</dt>
<dd>Change the <code>+</code> sign before variable names.</dd>
<dt>...</dt>
<dd>Further optional arguments.</dd></dl>

Arguments

Assessing confounding effects using Linear regression models — chest_lm

<dl>

<dt>crude</dt>
<dd>An object of formula for initial model, generally crude model.
However, any other variables can also be included here as the initial model.</dd>


<dt>xlist</dt>
<dd>A vector of characters with all variable names of potential confounders.</dd>


<dt>data</dt>
<dd>Data frame.</dd>


<dt>method</dt>
<dd>The method to be used; see 'lm'.</dd>


<dt>na_omit</dt>
<dd>Remove all missing values.</dd>


<dt>indicate</dt>
<dd>indicate progress</dd>


<dt>plus</dt>
<dd>Change the <code>+</code> sign before variable names.</dd>


<dt>...</dt>
<dd>Further optional arguments.</dd>

</dl>

chest_lm: Assessing confounding effects using Linear regression models

Description

Usage

Value

Arguments

See Also

Examples